log metrics for correct dataloader only

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -154,6 +154,19 @@ def update_eval_step_metrics(self) -> None:
         # increment the step even if nothing was logged
         self._increment_eval_log_step()
 
+    @staticmethod
+    def _filter_metrics_for_dataloader(
+        dl_idx: int, metrics: Dict[str, Union[Any, Dict[str, Any]]], metric_prefix: str = "dataloader_idx"
+    ) -> Dict[str, Union[Any, Dict[str, Any]]]:
+        result = {}
+        for k, v in metrics.items():
+            if metric_prefix not in k:
+                result[k] = v
+                continue
+            if k.endswith(f"{metric_prefix}_{dl_idx}"):
+                result[k] = v
+        return result
+
     def _prepare_eval_loop_results(self, metrics: _OUT_DICT) -> None:
         if self.trainer.sanity_checking:
             return
@@ -162,9 +175,7 @@ def _prepare_eval_loop_results(self, metrics: _OUT_DICT) -> None:
         has_been_initialized = len(self.eval_loop_results) == num_dataloaders
         for dl_idx in range(self.trainer._evaluation_loop.num_dataloaders):
             # remove callback metrics that don't belong to this dataloader
-            callback_metrics = {
-                k: v for k, v in metrics.items() if "dataloader_idx" not in k or f"dataloader_idx_{dl_idx}" in k
-            }
+            callback_metrics = self._filter_metrics_for_dataloader(dl_idx, metrics)
             if has_been_initialized:
                 self.eval_loop_results[dl_idx].update(callback_metrics)
             else:

tests/trainer/logging_/test_eval_loop_logging.py

@@ -23,6 +23,7 @@
 
 from pytorch_lightning import callbacks, Trainer
 from pytorch_lightning.loggers import TensorBoardLogger
+from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel, RandomDataset
 
@@ -672,3 +673,29 @@ def val_dataloader(self):
         enable_model_summary=False,
     )
     trainer.fit(model)
+
+
+@pytest.mark.parametrize(
+    ["kwargs", "expected"],
+    [
+        ({"dl_idx": 0, "metrics": {"acc": 123}}, {"acc": 123}),
+        (
+            {"dl_idx": 0, "metrics": {"acc/dataloader_idx_0": 123, "acc/dataloader_idx_1": 321}},
+            {"acc/dataloader_idx_0": 123},
+        ),
+        (
+            {"dl_idx": 10, "metrics": {"acc/dataloader_idx_1": 123, "acc/dataloader_idx_10": 321}},
+            {"acc/dataloader_idx_10": 321},
+        ),
+        (
+            {"dl_idx": 3, "metrics": {"top_3_acc/dataloader_idx_0": 123, "top_3_acc/dataloader_idx_3": 321}},
+            {"top_3_acc/dataloader_idx_3": 321},
+        ),
+        # theoretical case, as `/dataloader_idx_3` would have been added
+        ({"dl_idx": 3, "metrics": {"top_3_acc": 123}}, {"top_3_acc": 123}),
+    ],
+)
+def test_filter_metrics_for_dataloader(kwargs, expected):
+    """Logged metrics should only include metrics from the concerned dataloader."""
+    actual = LoggerConnector._filter_metrics_for_dataloader(**kwargs)
+    assert actual == expected